A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior

Rajat Budhiraja, Manish Kumar, Mrinal K Das, Anil Singh Bafila and Sanjeev Singh

PLOS ONE, 2021, vol. 16, issue 2, 1-24

Abstract: Significant research in reservoir computing over the past two decades has revived interest in recurrent neural networks. Owing to its ingrained capability of performing high-speed and low-cost computations this has become a panacea for multi-variate complex systems having non-linearity within their relationships. Modelling economic and financial trends has always been a challenging task owing to their volatile nature and no linear dependence on associated influencers. Prior studies aimed at effectively forecasting such financial systems, but, always left a visible room for optimization in terms of cost, speed and modelling complexities. Our work employs a reservoir computing approach complying to echo-state network principles, along with varying strengths of time-delayed feedback to model a complex financial system. The derived model is demonstrated to act robustly towards influence of trends and other fluctuating parameters by effectively forecasting long-term system behavior. Moreover, it also re-generates the financial system unknowns with a high degree of accuracy when only limited future data is available, thereby, becoming a reliable feeder for any long-term decision making or policy formulations.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0246737

DOI: 10.1371/journal.pone.0246737

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